CAREER: Foundations of Human-Centered Machine Learning in the Wild

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

Machine learning models today must operate amid increasingly dynamic and open environments. One important characteristic of open environments is that the intelligent system will encounter new contexts and out-of-distribution data; that is, data that were not used to train the algorithms the original system. However, current supervised learning is brittle, lacking a reliable understanding of the way data evolves over time and how to respond to changing environments. This introduces a set of new challenges and drives the need for rethinking the design of machine learning algorithms, that can detect out-of-distribution data and repair the learned model under evolving data in the wild. The project will directly impact many real-world domains including autonomous transportation, healthcare, commerce, and scientific discovery. The objective of this project is to lay new foundations for safe, adaptive, and long-term beneficial learning algorithms in open-world environments. The project has a continuum of three research aims that will fundamentally transform the way that machine learning models are trained, updated, and monitored in the wild: (1) create a new learning framework for reliable decisions, rendering strong safety against unknowns upon deploying machine learning models in the wild; (2) accelerate model adaptation, learning to classify new concepts emerging in the wild while minimizing human supervision required; (3) characterize and understand dynamics in terms of long-term accuracy and safety, maximizing the impact of models as they evolve and operate in the long run. The education plan will publicize the power of open-world machine learning through a new course, a new undergraduate mentorship program 'Entering AI Research' and outreach efforts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.